| Academic Year |
2026Year |
School/Graduate School |
Graduate School of Biomedical and Health Sciences (Master’s Course) |
| Lecture Code |
TB000143 |
Subject Classification |
Specialized Education |
| Subject Name |
特別演習 |
Subject Name
（Katakana） |
トクベツエンシュウ |
Subject Name in
English |
Seminar |
| Instructor |
CHIKAZOE JUNICHI,PHAM QUANG TRUNG |
Instructor
(Katakana) |
チカゾエ ジュンイチ,ファム クアン チュン |
| Campus |
Kasumi |
Semester/Term |
1st-Year, Second Semester, Second Semester |
| Days, Periods, and Classrooms |
(2nd) Inte |
| Lesson Style |
Seminar |
Lesson Style
(More Details) |
Face-to-face, Online (simultaneous interactive), Online (on-demand) |
| Hands-on, Discussion-based, Student Presentations, Practical Assignments, Programming |
| Credits |
2.0 |
Class Hours/Week |
|
Language of Instruction |
B
:
Japanese／English |
| Course Level |
5
:
Graduate Basic
|
| Course Area（Area） |
26
:
Biological and Life Sciences |
| Course Area（Discipline） |
04
:
Life Sciences |
| Eligible Students |
|
| Keywords |
|
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Class Objectives
/Class Outline |
The primary goal is to learn a wide range of machine learning algorithms, including deep learning, in order to select the most appropriate one according to the data's characteristics. This involves analyzing data by applying everything from basic machine learning techniques to advanced deep learning models, such as Large Language Models (LLMs), and being able to correctly interpret the results of these analyses. |
| Class Schedule |
lesson1 Orientation for the Second Term
lesson2 Brain-Inspired AI and Modeling
lesson3 Decoding and Encoding Analysis
lesson4 Representational Similarity Analysis (RSA)
lesson5 Foundations of Multimodal Analysis
lesson6 Generative AI and the Brain
lesson7 Data Analysis Workshop 1
lesson8 Data Analysis Workshop 2
lesson9 fData Analysis Workshop 3
lesson10 LLM Application Workshop 1
lesson11 LLM Application Workshop 2
lesson12 LLM Application Workshop 3
lesson13 Preparation for Mini-Project Presentations
lesson14 Mini-Project Presentations
lesson15 Deep Summary (Future research directions and integration) |
Text/Reference
Books,etc. |
The Elements of Statistical Learning (Hastie et al.)
Functional Magnetic Resonance Imaging (Huettel et al.) |
PC or AV used in
Class,etc. |
Text, Handouts, Microsoft Teams, Zoom |
| (More Details) |
|
| Learning techniques to be incorporated |
Discussions, PBL (Problem-based Learning)/ TBL (Team-based Learning), Project Learning |
Suggestions on
Preparation and
Review |
fMRI is an extremely useful research method that allows us to non-invasively investigate the cognitive functions of living humans. In particular, resting-state fMRI (rs-fMRI) is known to be useful for diagnosing diseases and is considered promising for clinical applications. It is essential not only to learn fMRI data analysis methods but also to understand the characteristics of this data in order to apply appropriate machine learning algorithms. |
| Requirements |
|
| Grading Method |
Evaluation will be based on the depth of understanding of the properties of fMRI data and the characteristics of machine learning algorithms. |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
|
| Other |
|
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |